def test_strategies(self): simplest_strategy_1 = SimplestChemenvStrategy() simplest_strategy_2 = SimplestChemenvStrategy(distance_cutoff=1.5, angle_cutoff=0.5) self.assertFalse(simplest_strategy_1 == simplest_strategy_2) simplest_strategy_1_from_dict = SimplestChemenvStrategy.from_dict( simplest_strategy_1.as_dict()) self.assertTrue(simplest_strategy_1, simplest_strategy_1_from_dict) effective_csm_estimator = { "function": "power2_inverse_decreasing", "options": { "max_csm": 8.0 }, } self_csm_weight = SelfCSMNbSetWeight() surface_definition = { "type": "standard_elliptic", "distance_bounds": { "lower": 1.1, "upper": 1.9 }, "angle_bounds": { "lower": 0.1, "upper": 0.9 }, } surface_definition_2 = { "type": "standard_elliptic", "distance_bounds": { "lower": 1.1, "upper": 1.9 }, "angle_bounds": { "lower": 0.1, "upper": 0.95 }, } da_area_weight = DistanceAngleAreaNbSetWeight( weight_type="has_intersection", surface_definition=surface_definition, nb_sets_from_hints="fallback_to_source", other_nb_sets="0_weight", additional_condition=DistanceAngleAreaNbSetWeight.AC.ONLY_ACB, ) da_area_weight_2 = DistanceAngleAreaNbSetWeight( weight_type="has_intersection", surface_definition=surface_definition_2, nb_sets_from_hints="fallback_to_source", other_nb_sets="0_weight", additional_condition=DistanceAngleAreaNbSetWeight.AC.ONLY_ACB, ) weight_estimator = { "function": "smootherstep", "options": { "delta_csm_min": 0.5, "delta_csm_max": 3.0 }, } symmetry_measure_type = "csm_wcs_ctwcc" delta_weight = DeltaCSMNbSetWeight( effective_csm_estimator=effective_csm_estimator, weight_estimator=weight_estimator, symmetry_measure_type=symmetry_measure_type, ) bias_weight = CNBiasNbSetWeight.linearly_equidistant(weight_cn1=1.0, weight_cn13=4.0) bias_weight_2 = CNBiasNbSetWeight.linearly_equidistant(weight_cn1=1.0, weight_cn13=5.0) angle_weight = AngleNbSetWeight() nad_weight = NormalizedAngleDistanceNbSetWeight( average_type="geometric", aa=1, bb=1) multi_weights_strategy_1 = MultiWeightsChemenvStrategy( dist_ang_area_weight=da_area_weight, self_csm_weight=self_csm_weight, delta_csm_weight=delta_weight, cn_bias_weight=bias_weight, angle_weight=angle_weight, normalized_angle_distance_weight=nad_weight, symmetry_measure_type=symmetry_measure_type, ) multi_weights_strategy_2 = MultiWeightsChemenvStrategy( dist_ang_area_weight=da_area_weight, self_csm_weight=self_csm_weight, delta_csm_weight=delta_weight, cn_bias_weight=bias_weight_2, angle_weight=angle_weight, normalized_angle_distance_weight=nad_weight, symmetry_measure_type=symmetry_measure_type, ) multi_weights_strategy_3 = MultiWeightsChemenvStrategy( dist_ang_area_weight=da_area_weight_2, self_csm_weight=self_csm_weight, delta_csm_weight=delta_weight, cn_bias_weight=bias_weight, angle_weight=angle_weight, normalized_angle_distance_weight=nad_weight, symmetry_measure_type=symmetry_measure_type, ) multi_weights_strategy_1_from_dict = MultiWeightsChemenvStrategy.from_dict( multi_weights_strategy_1.as_dict()) self.assertTrue( multi_weights_strategy_1 == multi_weights_strategy_1_from_dict) self.assertFalse(simplest_strategy_1 == multi_weights_strategy_1) self.assertFalse(multi_weights_strategy_1 == multi_weights_strategy_2) self.assertFalse(multi_weights_strategy_1 == multi_weights_strategy_3) self.assertFalse(multi_weights_strategy_2 == multi_weights_strategy_3)
def test_strategies(self): simplest_strategy_1 = SimplestChemenvStrategy() simplest_strategy_2 = SimplestChemenvStrategy(distance_cutoff=1.5, angle_cutoff=0.5) self.assertFalse(simplest_strategy_1 == simplest_strategy_2) simplest_strategy_1_from_dict = SimplestChemenvStrategy.from_dict(simplest_strategy_1.as_dict()) self.assertTrue(simplest_strategy_1, simplest_strategy_1_from_dict) effective_csm_estimator = {'function': 'power2_inverse_decreasing', 'options': {'max_csm': 8.0}} self_csm_weight = SelfCSMNbSetWeight() surface_definition = {'type': 'standard_elliptic', 'distance_bounds': {'lower': 1.1, 'upper': 1.9}, 'angle_bounds': {'lower': 0.1, 'upper': 0.9}} surface_definition_2 = {'type': 'standard_elliptic', 'distance_bounds': {'lower': 1.1, 'upper': 1.9}, 'angle_bounds': {'lower': 0.1, 'upper': 0.95}} da_area_weight = DistanceAngleAreaNbSetWeight(weight_type='has_intersection', surface_definition=surface_definition, nb_sets_from_hints='fallback_to_source', other_nb_sets='0_weight', additional_condition=DistanceAngleAreaNbSetWeight.AC.ONLY_ACB) da_area_weight_2 = DistanceAngleAreaNbSetWeight(weight_type='has_intersection', surface_definition=surface_definition_2, nb_sets_from_hints='fallback_to_source', other_nb_sets='0_weight', additional_condition=DistanceAngleAreaNbSetWeight.AC.ONLY_ACB) weight_estimator = {'function': 'smootherstep', 'options': {'delta_csm_min': 0.5, 'delta_csm_max': 3.0}} symmetry_measure_type = 'csm_wcs_ctwcc' delta_weight = DeltaCSMNbSetWeight(effective_csm_estimator=effective_csm_estimator, weight_estimator=weight_estimator, symmetry_measure_type=symmetry_measure_type) bias_weight = CNBiasNbSetWeight.linearly_equidistant(weight_cn1=1.0, weight_cn13=4.0) bias_weight_2 = CNBiasNbSetWeight.linearly_equidistant(weight_cn1=1.0, weight_cn13=5.0) angle_weight = AngleNbSetWeight() nad_weight = NormalizedAngleDistanceNbSetWeight(average_type='geometric', aa=1, bb=1) multi_weights_strategy_1 = MultiWeightsChemenvStrategy(dist_ang_area_weight=da_area_weight, self_csm_weight=self_csm_weight, delta_csm_weight=delta_weight, cn_bias_weight=bias_weight, angle_weight=angle_weight, normalized_angle_distance_weight=nad_weight, symmetry_measure_type=symmetry_measure_type) multi_weights_strategy_2 = MultiWeightsChemenvStrategy(dist_ang_area_weight=da_area_weight, self_csm_weight=self_csm_weight, delta_csm_weight=delta_weight, cn_bias_weight=bias_weight_2, angle_weight=angle_weight, normalized_angle_distance_weight=nad_weight, symmetry_measure_type=symmetry_measure_type) multi_weights_strategy_3 = MultiWeightsChemenvStrategy(dist_ang_area_weight=da_area_weight_2, self_csm_weight=self_csm_weight, delta_csm_weight=delta_weight, cn_bias_weight=bias_weight, angle_weight=angle_weight, normalized_angle_distance_weight=nad_weight, symmetry_measure_type=symmetry_measure_type) multi_weights_strategy_1_from_dict = MultiWeightsChemenvStrategy.from_dict(multi_weights_strategy_1.as_dict()) self.assertTrue(multi_weights_strategy_1 == multi_weights_strategy_1_from_dict) self.assertFalse(simplest_strategy_1 == multi_weights_strategy_1) self.assertFalse(multi_weights_strategy_1 == multi_weights_strategy_2) self.assertFalse(multi_weights_strategy_1 == multi_weights_strategy_3) self.assertFalse(multi_weights_strategy_2 == multi_weights_strategy_3)
def test_strategies(self): simplest_strategy_1 = SimplestChemenvStrategy() simplest_strategy_2 = SimplestChemenvStrategy(distance_cutoff=1.5, angle_cutoff=0.5) self.assertFalse(simplest_strategy_1 == simplest_strategy_2) simplest_strategy_1_from_dict = SimplestChemenvStrategy.from_dict( simplest_strategy_1.as_dict()) self.assertTrue(simplest_strategy_1, simplest_strategy_1_from_dict) effective_csm_estimator = { 'function': 'power2_inverse_decreasing', 'options': { 'max_csm': 8.0 } } self_csm_weight = SelfCSMNbSetWeight() surface_definition = { 'type': 'standard_elliptic', 'distance_bounds': { 'lower': 1.1, 'upper': 1.9 }, 'angle_bounds': { 'lower': 0.1, 'upper': 0.9 } } surface_definition_2 = { 'type': 'standard_elliptic', 'distance_bounds': { 'lower': 1.1, 'upper': 1.9 }, 'angle_bounds': { 'lower': 0.1, 'upper': 0.95 } } da_area_weight = DistanceAngleAreaNbSetWeight( weight_type='has_intersection', surface_definition=surface_definition, nb_sets_from_hints='fallback_to_source', other_nb_sets='0_weight', additional_condition=DistanceAngleAreaNbSetWeight.AC.ONLY_ACB) da_area_weight_2 = DistanceAngleAreaNbSetWeight( weight_type='has_intersection', surface_definition=surface_definition_2, nb_sets_from_hints='fallback_to_source', other_nb_sets='0_weight', additional_condition=DistanceAngleAreaNbSetWeight.AC.ONLY_ACB) weight_estimator = { 'function': 'smootherstep', 'options': { 'delta_csm_min': 0.5, 'delta_csm_max': 3.0 } } symmetry_measure_type = 'csm_wcs_ctwcc' delta_weight = DeltaCSMNbSetWeight( effective_csm_estimator=effective_csm_estimator, weight_estimator=weight_estimator, symmetry_measure_type=symmetry_measure_type) bias_weight = CNBiasNbSetWeight.linearly_equidistant(weight_cn1=1.0, weight_cn13=4.0) bias_weight_2 = CNBiasNbSetWeight.linearly_equidistant(weight_cn1=1.0, weight_cn13=5.0) angle_weight = AngleNbSetWeight() nad_weight = NormalizedAngleDistanceNbSetWeight( average_type='geometric', aa=1, bb=1) multi_weights_strategy_1 = MultiWeightsChemenvStrategy( dist_ang_area_weight=da_area_weight, self_csm_weight=self_csm_weight, delta_csm_weight=delta_weight, cn_bias_weight=bias_weight, angle_weight=angle_weight, normalized_angle_distance_weight=nad_weight, symmetry_measure_type=symmetry_measure_type) multi_weights_strategy_2 = MultiWeightsChemenvStrategy( dist_ang_area_weight=da_area_weight, self_csm_weight=self_csm_weight, delta_csm_weight=delta_weight, cn_bias_weight=bias_weight_2, angle_weight=angle_weight, normalized_angle_distance_weight=nad_weight, symmetry_measure_type=symmetry_measure_type) multi_weights_strategy_3 = MultiWeightsChemenvStrategy( dist_ang_area_weight=da_area_weight_2, self_csm_weight=self_csm_weight, delta_csm_weight=delta_weight, cn_bias_weight=bias_weight, angle_weight=angle_weight, normalized_angle_distance_weight=nad_weight, symmetry_measure_type=symmetry_measure_type) multi_weights_strategy_1_from_dict = MultiWeightsChemenvStrategy.from_dict( multi_weights_strategy_1.as_dict()) self.assertTrue( multi_weights_strategy_1 == multi_weights_strategy_1_from_dict) self.assertFalse(simplest_strategy_1 == multi_weights_strategy_1) self.assertFalse(multi_weights_strategy_1 == multi_weights_strategy_2) self.assertFalse(multi_weights_strategy_1 == multi_weights_strategy_3) self.assertFalse(multi_weights_strategy_2 == multi_weights_strategy_3)